Data Science Meets Behavioral Science

Sarah Van Caster

(Dooder/Shutterstock)

In the United States alone, 38 million people start their day by eagerly fastening a device to their wrist that is not worn for the purpose of fashion or keeping time. It is a fitness tracker and these little gadgets have swept the nation. Why? Because people love having instant access to their performance, activities and goals. They enjoy tracking their progress throughout the day. They are addicted to the gratifying notifications of success, and the social aspects of competing with friends, family members, and coworkers.

The fitness tracker market has achieved tremendous success by providing its consumers with relevant data and motivating incentives. They are successfully inspiring the world to be more active by leveraging principles from both data science and behavioral science.

For centuries, traditional economic theory dictated that humans make logical, self-interested decisions, always choosing the most favorable conditions. However, reality often demonstrates otherwise.

Every January, how many people do you know say that they want to resolve to save more, spend less, eat better, or exercise more? These admirable goals are often proclaimed with the best of intentions, but are rarely achieved. If people were purely logical, we would all be the healthiest versions of ourselves.

However, the truth is that humans are not 100% rational; we are emotional creatures that are not always predictable. Behavioral economics evolved from this recognition of human irrationality. Behavioral economics is a method of economic analysis that applies psychological insights into human behavior to explain economic decision-making.

Essentially, it is the intersection between economics and behavioral psychology. Behavioral economics helps us understand why only one-third of Americans floss daily, why most people’s expensive home treadmills turn into overpriced coat racks, and why motivating humans is more complicated than ever before.

Traditional economic theory does not address human irrationality

Human behavior can be seen as the byproduct of millions of years of evolution. With a nature forged from hunger, anxiety and fear, it is no wonder the behaviors of modern man can often be irrational – driven by forces like peer pressure, availability bias and emotional exhaustion. To change human behavior, we must embrace our human nature, instead of fight it. And one of the most powerful tools to help enable change is data.

Data science is the discipline that allows us to analyze the unseen – and with machine learning, it allows us to look at large sets of data and surface patterns, identifying when past performance is indicative of future results. For instance, it lets us forecast what products are most likely to be sold and which customers are most likely to buy. But what if you not only want to understand potential outcomes, what if you want to completely change outcomes, and more specifically, what if you want to change the way in which people behave? Behavioral economics tells us that to make a fundamental change in behavior that will affect the long-term outcome of a process, we must insert an inflection point. What is the best method to create an inflection point or get someone to do something they would not ordinarily do? Incentives.

As an example, you are a sales rep and two years ago your revenue was $1 million. Last year it was $1.1 million, and this year you expect $1.2 million in sales. The trend is clear, and your growth has been linear and predictable. However, there is a change in company leadership and your management has increased your quota to $2 million for next year. What is going to motivate you to almost double your revenues? The difference between expectations ($2 million) and reality ($1.2 million) is often referred to as the “behavioral gap” (see chart below).

When the behavioral gap is significant, an inflection point is needed to close that gap. The right incentive can initiate an inflection point and influence a change in behavior. Perhaps that incentive is an added bonus, President’s Club eligibility, a promotion, etc.

The behavior gap depicted above represents the difference between raised expectations (management increasing quota) and the trajectory of current sales performance.

In the US, studies from Harvard Business Review and other industry publications posit that companies spend over one trillion dollars annually on incentives. That number is four times the money spent on advertising in the US annually. What that means is that, as a nation, we are deeply invested in incenting people to act in ways that are somewhat contrary to how they would normally act, if left to their own devices. Incentives appear in many forms such as commissions and bonuses for sales personnel and channel sellers, rebate payments and marketing incentives for partners and customers, and promotions, discounts and coupons for end consumers.

Incentives are most effective when they are intelligent, or data driven. Deloitte University Press published a report stating that when it comes to the relationship between data science and behavioral science, “it is reasonable to anticipate better results when the two approaches are treated as complementary and applied in tandem. Behavioral science principles should be part of the data scientist’s toolkit, and vice versa.”

Data scientists work with product and sales teams, employing data and patterns to manage incentive programs. Using forecast modeling and behavior mechanics, teams can plot out the path from one goal to the next and analyze and implement proper incentives.

As an example, let’s say your company is a furniture manufacturer that uses a CPQ tool to manage its complex quoting and pricing processes. One of the major reasons your company invested in the CPQ solution was to curb chronic, costly discounting by the sales team.

You are a new sales rep using CPQ to build a quote. What if, mid-quote, your system alerts you that the discount you entered, while within the approved range, may not be ideal. Machine learning ran in the background and identified a different discount used by the top 10% of reps that has had more success. Additionally, you learn that if you choose the prescribed discount, you will earn 40% more commission! Talk about a relevant incentive, based on powerful data.

In a real-world implementation, one Quote-to-Cash customer – let’s call them Company X – who links websites with advertisers, needed to be able to better forecast the potential revenue for each deal. The nature of the business does not allow Company X to recognize revenue until a user clicks on an ad. They harnessed machine learning to understand past behavior, used behavioral science to influence future behavior, and implemented A/B testing (comparing two versions of a web page to see which performs better) on incentive effectiveness programs. The A/B testing data allowed Company X to understand the effectiveness of certain incentives to guide customer behavior.

About the author: Sarah Van Caster is a Data Analyst at Apttus and Lead Strategist for Incentives. She has decade of experience in high-tech, communications and logistics industries and she enjoys designing innovative, customer-focused content and solutions. Sarah has degrees from the University of Wisconsin and Drake University.